@inproceedings{seonwoo-etal-2019-additive,
title = "Additive Compositionality of Word Vectors",
author = "Seonwoo, Yeon and
Park, Sungjoon and
Kim, Dongkwan and
Oh, Alice",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5551",
doi = "10.18653/v1/D19-5551",
pages = "387--396",
abstract = "Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives. Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution. In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions. We then empirically show our model{'}s improved semantic representation performance on word similarity and noisy sentence similarity.",
}
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%0 Conference Proceedings
%T Additive Compositionality of Word Vectors
%A Seonwoo, Yeon
%A Park, Sungjoon
%A Kim, Dongkwan
%A Oh, Alice
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F seonwoo-etal-2019-additive
%X Additive compositionality of word embedding models has been studied from empirical and theoretical perspectives. Existing research on justifying additive compositionality of existing word embedding models requires a rather strong assumption of uniform word distribution. In this paper, we relax that assumption and propose more realistic conditions for proving additive compositionality, and we develop a novel word and sub-word embedding model that satisfies additive compositionality under those conditions. We then empirically show our model’s improved semantic representation performance on word similarity and noisy sentence similarity.
%R 10.18653/v1/D19-5551
%U https://aclanthology.org/D19-5551
%U https://doi.org/10.18653/v1/D19-5551
%P 387-396
Markdown (Informal)
[Additive Compositionality of Word Vectors](https://aclanthology.org/D19-5551) (Seonwoo et al., WNUT 2019)
ACL
- Yeon Seonwoo, Sungjoon Park, Dongkwan Kim, and Alice Oh. 2019. Additive Compositionality of Word Vectors. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 387–396, Hong Kong, China. Association for Computational Linguistics.